Edge device development support apparatus and method

Abstract

The present invention relates to an edge device development support apparatus and method, and the edge device development support apparatus includes a user interface unit configured to provide a user interface, and a processor configured to execute an artificial intelligence model on hardware to be used in an edge device, estimate the performance of the hardware, calculate the cost of the hardware that is incurred by utilizing the hardware, then select hardware according to the performance and the cost, and output the selected hardware through the user interface.

What is claimed is: 
     
         1 . An edge device development support apparatus comprising:
 a user interface unit configured to provide a user interface; and   a processor configured to execute an artificial intelligence model on hardware to be used in an edge device, estimate performance of the hardware, calculate a cost of the hardware that is incurred by utilizing the hardware, then select hardware according to the performance and the cost, and output the selected hardware through the user interface.   
     
     
         2 . The edge device development support apparatus of  claim 1 , wherein the processor receives the artificial intelligence model through the user interface unit or receives data for a function and training of the artificial intelligence model through the user interface unit, and generates the artificial intelligence model through automated machine learning (AutoML). 
     
     
         3 . The edge device development support apparatus of  claim 2 , wherein the AutoML searches for a structure of an artificial intelligence model that takes a hardware structure of the edge device into consideration and lightens or optimizes the artificial intelligence model in consideration of performance of the edge device. 
     
     
         4 . The edge device development support apparatus of  claim 1 , wherein the processor determines whether the performance satisfies a preset performance requirement by reflecting an error in the performance estimation. 
     
     
         5 . The edge device development support apparatus of  claim 1 , wherein the processor estimates the performance by executing the artificial intelligence model on an actual hardware platform, estimates the performance using a hardware structure and artificial intelligence model analysis, estimates the performance using a lookup table for performance estimation that is stored for each piece of hardware, or estimates the performance using a machine learning model developed based on performance data obtained by executing the artificial intelligence model on actual hardware. 
     
     
         6 . The edge device development support apparatus of  claim 1 , wherein the processor calculates a cost of the hardware whose performance satisfies the preset performance requirement among the hardware. 
     
     
         7 . The edge device development support apparatus of  claim 1 , wherein the processor calculates the cost of the hardware by reflecting preset cost conditions for each piece of hardware. 
     
     
         8 . The edge device development support apparatus of  claim 7 , wherein the cost conditions include at least one of price, development difficulty, and hardware development support environment. 
     
     
         9 . The edge device development support apparatus of  claim 8 , wherein the price is normalized based on prices for released hardware products, and the development difficulty and the hardware development support environment are defined by grade and normalized. 
     
     
         10 . The edge device development support apparatus of  claim 1 , wherein the processor selects hardware by reflecting a weight on at least one of the performance and the cost. 
     
     
         11 . An edge device development support method comprising:
 executing, by a processor, an artificial intelligence model on hardware to be used in an edge device and estimating performance of the hardware;   calculating, by the processor, a cost of the hardware that is incurred by utilizing the hardware; and   selecting, by the processor, hardware according to the performance and the cost.   
     
     
         12 . The edge device development support method of  claim 11 , wherein, in the estimating of the performance of the hardware, the processor receives the artificial intelligence model through a user interface unit or receives data for a function and training of the artificial intelligence model through the user interface unit, and generates the artificial intelligence model through automated machine learning (AutoML). 
     
     
         13 . The edge device development support method of  claim 12 , wherein the AutoML searches for a structure of an artificial intelligence model that takes a hardware structure of the edge device into consideration and lightens or optimizes the artificial intelligence model in consideration of performance of the edge device. 
     
     
         14 . The edge device development support method of  claim 11 , wherein, in the estimating of the performance of the hardware, the processor determines whether the performance satisfies a preset performance requirement by reflecting an error in the performance estimation. 
     
     
         15 . The edge device development support method of  claim 11 , wherein, in the estimating of the performance of the hardware, the processor estimates the performance by executing the artificial intelligence model on an actual hardware platform, estimates the performance using a hardware structure and artificial intelligence model analysis, estimates the performance using a lookup table for performance estimation that is stored for each piece of hardware, or estimates the performance using a machine learning model developed based on performance data obtained by executing the artificial intelligence model on actual hardware. 
     
     
         16 . The edge device development support method of  claim 11 , wherein, in the calculating of the cost of the hardware, the processor calculates a cost of the hardware whose performance satisfies the preset performance requirement among the hardware. 
     
     
         17 . The edge device development support method of  claim 11 , wherein, in the calculating of the cost of the hardware, the processor calculates the cost of the hardware by reflecting preset cost conditions for each piece of hardware. 
     
     
         18 . The edge device development support method of  claim 17 , wherein the cost conditions include at least one of price, development difficulty, and hardware development support environment. 
     
     
         19 . The edge device development support method of  claim 18 , wherein the price is normalized based on prices for released hardware products, and the development difficulty and the hardware development support environment are defined by grade and normalized. 
     
     
         20 . The edge device development support method of  claim 11 , wherein, in the selecting of the hardware, the processor selects hardware by reflecting a weight on at least one of the performance and the cost.